The Elements of Big Data Value : (Record no. 307803)
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| 000 -LEADER | |
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| fixed length control field | 11110nam a22004933i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC6676594 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240122001413.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 231124s2021 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783030681760 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9783030681753 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC6676594 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL6676594 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1258660409 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA75.5-76.95 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Curry, Edward. |
| 245 14 - TITLE STATEMENT | |
| Title | The Elements of Big Data Value : |
| Remainder of title | Foundations of the Research and Innovation Ecosystem. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cham : |
| Name of producer, publisher, distributor, manufacturer | Springer International Publishing AG, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | �2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (412 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Intro -- Foreword -- Foreword -- Foreword -- Preface -- Acknowledgements -- Contents -- Editors and Contributors -- Part I: Ecosystem Elements of Big Data Value -- The European Big Data Value Ecosystem -- 1 Introduction -- 2 What Is Big Data Value? -- 3 Strategic Importance of Big Data Value -- 4 Developing a European Big Data Value Ecosystem -- 4.1 Challenges -- 4.2 A Call for Action -- 4.3 The Big Data Value PPP (BDV PPP) -- 4.4 Big Data Value Association -- 5 The Elements of Big Data Value -- 5.1 Ecosystem Elements of Big Data Value -- 5.2 Research and Innovation Elements of Big Data Value -- 5.3 Business, Policy and Societal Elements of Big Data Value -- 5.4 Emerging Elements of Big Data Value -- 6 Summary -- References -- Stakeholder Analysis of Data Ecosystems -- 1 Introduction -- 2 Stakeholder Analysis -- 3 Who Is a Stakeholder? -- 4 Methodology -- 4.1 Phase 1: Case Studies -- 4.2 Phase 2: Cross-Case Analysis -- 5 Sectoral Case Studies -- 6 Cross-Case Analysis -- 6.1 Technology Adoption Stage -- 6.2 Data Value Chain -- 6.3 Strategic Impact of IT -- 6.4 Stakeholder Characteristics -- 6.5 Stakeholder Influence -- 7 Summary -- References -- A Roadmap to Drive Adoption of Data Ecosystems -- 1 Introduction -- 2 Challenges for the Adoption of Big Data Value -- 3 Big Data Value Public-Private Partnership -- 3.1 The Big Data Value Ecosystem -- 4 Five Mechanism to Drive Adoption -- 4.1 European Innovation Spaces (i-Spaces) -- 4.2 Lighthouse Projects -- 4.3 Technical Projects -- 4.4 Platforms for Data Sharing -- 4.4.1 Industrial Data Platforms (IDP) -- 4.4.2 Personal Data Platforms (PDP) -- 4.5 Cooperation and Coordination Projects -- 5 Roadmap for Adoption of Big Data Value -- 6 European Data Value Ecosystem Development -- 7 Summary -- References -- Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 1 Introduction -- 2 The Big Data Value PPP -- 2.1 BDV PPP Vision and Objectives for European Big Data Value -- 2.2 Big Data Value Association (BDVA) -- 2.3 BDV PPP Objectives -- 2.4 BDV PPP Governance -- 2.5 BDV PPP Monitoring Framework -- 3 Main Activities and Achievements During 2018 -- 3.1 Mobilisation of Stakeholders, Outreach, Success Stories -- 4 Monitored Achievements and Impact of the PPP -- 4.1 Achievement of the Goals of the PPP -- 4.2 Progress Achieved on KPIs -- 4.2.1 Private Investments -- 4.2.2 Job Creation, New Skills and Job Profiles -- 4.2.3 Impact of the BDV PPP on SMEs -- 4.2.4 Innovations Emerging from Projects -- 4.2.5 Supporting Major Sectors and Major Domains with Big Data Technologies and Applications -- 4.2.6 Experimentation -- 4.2.7 SRIA Implementation and Update -- 4.2.8 Technical Projects -- 4.2.9 Macro-economic KPIs -- 4.2.10 Contributions to Environmental Challenges -- 4.2.11 Standardisation Activities with European Standardisation Bodies -- 5 Summary and Outlook -- References -- Part II: Research and Innovation Elements of Big Data Value -- Technical Research Priorities for Big Data -- 1 Introduction -- 2 Methodology -- 2.1 Technology State of the Art and Sector Analysis -- 2.2 Subject Matter Expert Interviews -- 2.3 Stakeholder Workshops -- 2.4 Requirement Consolidation -- 2.5 Community Survey -- 3 Research Priorities for Big Data Value -- 3.1 Priority `Data Management� -- 3.1.1 Challenges -- 3.1.2 Outcomes -- 3.2 Priority `Data Processing Architectures� -- 3.2.1 Challenges -- 3.2.2 Outcomes -- 3.3 Priority `Data Analytics� -- 3.3.1 Challenges -- 3.3.2 Outcomes -- 3.4 Priority `Data Visualisation and User Interaction� -- 3.4.1 Challenges -- 3.4.2 Outcomes -- 3.5 Priority `Data Protection� -- 3.5.1 Challenges -- 3.5.2 Outcomes -- 4 Big Data Standardisation -- 5 Engineering and DevOps for Big Data -- 5.1 Challenges. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 5.2 Outcomes -- 6 Illustrative Scenario in Healthcare -- 7 Summary -- References -- A Reference Model for Big Data Technologies -- 1 Introduction -- 2 Reference Model -- 2.1 Horizontal Concerns -- 2.1.1 Data Visualisation and User Interaction -- 2.1.2 Data Analytics -- 2.1.3 Data Processing Architectures -- 2.1.4 Data Protection -- 2.1.5 Data Management -- 2.1.6 Cloud and High-Performance Computing (HPC) -- 2.1.7 IoT, CPS, Edge and Fog Computing -- 2.2 Vertical Concerns -- 2.2.1 Big Data Types and Semantics -- 2.2.2 Standards -- 2.2.3 Communication and Connectivity -- 2.2.4 Cybersecurity -- 2.2.5 Engineering and DevOps for Building Big Data Value Systems -- 2.2.6 Marketplaces, Industrial Data Platforms and Personal Data Platforms (IDPs/PDPs), Ecosystems for Data Sharing and Innovat... -- 3 Transforming Transport Case Study -- 3.1 Data Analytics -- 3.2 Data Visualisation -- 3.3 Data Management -- 3.4 Assessing the Impact of Big Data Technologies -- 3.5 Use Case Conclusion -- 4 Summary -- References -- Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving T... -- 1 Introduction -- 1.1 Aim of the Chapter -- 1.2 Context -- 2 Challenges to Security and Privacy in Big Data -- 3 Current Trends and Solutions in Privacy-Preserving Technologies -- 3.1 Trend 1: User-Centred Data Protection -- 3.2 Trend 2: Automated Compliance and Tools for Transparency -- 3.3 Trend 3: Learning with Big Data in a Privacy-Friendly and Confidential Way -- 3.4 Future Direction for Policy and Technology Development: Implementing the Old and Developing the New -- 4 Recommendations for Privacy-Preserving Technologies -- References -- A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence -- 1 Introduction -- 2 Innovation Ecosystems and Centres of Excellence. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 2.1 What Are Centres of Excellence? -- 3 Methodology -- 4 Best Practice Framework for Big Data and Artificial Intelligence Centre of Excellence -- 4.1 Environment -- 4.1.1 Industry -- 4.1.2 Policy -- 4.1.3 Societal -- 4.2 Strategic Capabilities -- 4.2.1 Strategy -- 4.2.2 Governance -- 4.2.3 Structure -- 4.2.4 Funding -- 4.2.5 People -- 4.2.6 Culture -- 4.3 Operational Capabilities -- 4.4 Impact -- 4.4.1 Economic Impact -- 4.4.2 Scientific Impact -- 4.4.3 Societal Impact -- 4.4.4 Impact Measured Through KPIs -- 5 How to Use the Framework -- 5.1 Framework in Action -- 6 Critical Success Factors for Centres of Excellence -- 6.1 Challenges -- 6.2 Success Factors -- 6.3 Mechanisms to Address Challenges -- 6.4 Ideal Situation -- 7 Summary -- References -- Data Innovation Spaces -- 1 Introduction -- 2 Introduction to the European Data Innovation Spaces -- 3 Key Elements of an i-Space -- 4 Role of an i-Space and its Alignment with Other Initiatives -- 5 BDVA i-Spaces Certification Process -- 6 Impact of i-Spaces in Their Local Innovation Ecosystems -- 7 Cross-Border Collaboration: Towards a European Federation of i-Spaces -- 8 Success Stories -- 8.1 CeADAR: Ireland�s Centre for Applied Artificial Intelligence -- 8.2 CINECA -- 8.3 EGI -- 8.4 EURECAT/Big Data CoE Barcelona -- 8.5 ITAINNOVA/Aragon DIH -- 8.6 ITI/Data Cycle Hub -- 8.7 Know-Center -- 8.8 NCSR Demokritos/Attica Hub for the Economy of Data and Devices (ahedd) -- 8.9 RISE/ICE by RISE -- 8.10 Smart Data Innovation Lab (SDIL) -- 8.11 TeraLab -- 8.12 Universidad Polit�ecnica de Madrid/Madrid�s i-Space for Sustainability/AIR4S DIH -- 9 Summary -- Reference -- Part III: Business, Policy, and Societal Elements of Big Data Value -- Big Data Value Creation by Example -- 1 Introduction -- 2 How Can Big Data Transform Everyday Mobility and Logistics?. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 3 Digitalizing Forestry by Harnessing the Power of Big Data -- 4 GATE: First Big Data Centre of Excellence in Bulgaria -- 5 Beyond Privacy: Ethical and Societal Implications of Data Science -- 6 A Three-Year Journey to Insights and Investment -- 7 Scaling Up Data-Centric Start-Ups -- 8 Campaign Booster -- 9 AI Technology Meets Animal Welfare to Sustainably Feed the World -- 10 Creating the Next Generation of Smart Manufacturing with Federated Learning -- 11 Towards Open and Agile Big Data Analytics in Financial Sector -- 12 Electric Vehicles for Humans -- 13 Enabling 5G in Europe -- 14 Summary -- References -- Business Models and Ecosystem for Big Data -- 1 Introduction -- 2 Big Data Business Approaches -- 2.1 Optimisation and Improvements -- 2.2 Upgrading and Revaluation -- 2.3 Monetising -- 2.4 Breakthrough -- 3 Data-Driven Business Opportunities -- 4 Leveraging the Data Ecosystems -- 4.1 Data-Sharing Ecosystem -- 4.2 Data Innovation Ecosystems -- 4.3 Value Networks in a Business Ecosystem -- 5 Data-Driven Innovation Framework and Success Stories -- 5.1 The Data-Driven Innovation Framework -- 5.2 Examples of Success Stories -- 5.2.1 Selectionnist -- 5.2.2 Arable -- 6 Conclusion -- References -- Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework -- 1 Introduction -- 2 Data-Driven Innovation -- 2.1 What Are Business Opportunities? -- 2.2 Characteristics of Data-Driven Innovation -- 2.3 How to Screen Data-Driven Innovation? -- 3 The ``Making-of�� the DDI Framework -- 3.1 State-of-the-Art Analysis -- 3.2 DDI Ontology Building -- 3.3 Data Collection and Coding -- 3.3.1 Selection Criteria -- 3.3.2 Sample Data Generation -- 3.3.3 Coding of Data -- 3.4 Data Analysis -- 4 Findings of the Empirical DDI Research Study -- 4.1 General Findings -- 4.2 Value Proposition -- 4.3 Data -- 4.4 Technology. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 4.5 Network Strategies. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Metzger, Andreas. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Zillner, Sonja. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Pazzaglia, Jean-Christophe. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Garc�ia Robles, Ana. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Curry, Edward |
| Title | The Elements of Big Data Value |
| Place, publisher, and date of publication | Cham : Springer International Publishing AG,c2021 |
| International Standard Book Number | 9783030681753 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=6676594">https://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=6676594</a> |
| Public note | Click to View |
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